Thyroid Cancer in Childhood: A Retrospective Review of Childhood Course
Bibliographic record
Abstract
BACKGROUND: Thyroid cancer (TC) is an uncommon childhood malignancy, but the incidence may be increasing. Recent American Thyroid Association guidelines focus primarily on adult data. Natural history studies of TC in childhood are important to determine outcomes. The objectives of this study were to describe the demographics and outcomes in children with TC treated at The Hospital for Sick Children, Toronto, from 1983 to 2006. We hypothesized that childhood TC was increasing at our institution. METHODS: Cases of papillary TC (PTC) (including follicular variant PTC) and follicular TC (FTC) were identified from pathology databases. Chart review was performed, and data were extracted on clinical, treatment, and outcome variables. RESULTS: Sixty-one cases were identified, and complete data were available in 54, including 36 girls and 18 boys. There was no statistical change in numbers of cases diagnosed yearly during the study period. Younger children were more likely to have metastases at presentation or during follow-up. Pathological TC diagnosis included 40 PTC, 1 diffuse-sclerosing papillary, 7 follicular variant PTC, and 6 FTC. There was no difference in pathology findings between children less than or greater than 10 years old. Five patients had a history of previous malignancy, and five had a history of previous thyroid conditions. Three patients were born in areas of high TC endemnicity. Twenty-three patients had thyroiditis on pathology examination. All patients underwent total thyroidectomy, and 53/54 patients received therapeutic radioactive iodine ablation. Twenty-seven patients had metastases at presentation (19 lymph nodes only, 2 lung only, and 6 lymph node and distant) and 6 developed distant metastases during follow-up (3 lung, 2 thymus, and 1 paraspinal). Male sex was associated with development of metastases during follow-up. On multiple regression, tumor size was predicted positively by PTC but not by age, sex, or metastases at presentation or during follow-up. CONCLUSION: We did not find evidence of increasing numbers of cases of TC diagnosed yearly during the study period, or difference in tumor aggressiveness, or between outcomes in children aged less than or greater than 10 years. Children with metastases at presentation or during follow-up were likely to be younger than children without metastases. There is a need for prospective, collaborative multicenter studies of TC.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".